Multimodal Execution Monitor

Assistive robots have the potential to serve as caregivers, assisting with activities of daily living (ADLs) and instrumental activities of daily living (IADLs). Detecting when something has gone wrong could help assistive robots operate more safely and effectively around people. However, the complexity of interacting with people and objects in human environments can make errors difficult to detect. I introduce a multimodal execution monitoring system to detect and classify anomalous executions when robots operate near humans. The system’s anomaly detector models multimodal sensory signals with a hidden Markov model (HMM) or an LSTM-VAE. The detector uses a likelihood threshold that varies based on the progress of task execution. The system classifies the type and cause of common anomalies using an artificial neural network. I evaluate my system with haptic, visual, auditory, and kinematic sensing during household tasks and human-robot interactive tasks (feeding assistance) performed by a PR2 robot with able-bodied participants and people with disabilities. In my evaluation, my methods performed better than other methods from the literature, yielding higher area under curve (AUC) and shorter detection delays. Multimodality also improved the performance of monitoring methods by detecting a broader range of anomalies.

Assistive Manipulation

General-purpose mobile manipulators have the potential to serve as a versatile form of assistive technology. However, their complexity creates challenges, including the risk of being too difficult to use. We present a proof-of-concept robotic system for assistive feeding that consists of a Willow Garage PR2, a high-level web-based interface, and specialized autonomous behaviors for scooping and feeding yogurt. As a step towards use by people with disabilities, we evaluated our system with 5 able-bodied participants. All 5 successfully ate yogurt using the system and reported high rates of success for the system's autonomous behaviors. Also, Henry Evans, a person with severe quadriplegia, operated the system remotely to feed an able-bodied person. In general, people who operated the system reported that it was easy to use, including Henry. The feeding system also incorporates corrective actions designed to be triggered either autonomously or by the user. In an offline evaluation using data collected with the feeding system, a new version of our multimodal anomaly detection system outperformed prior versions.